Method for ambiguity resolution in location determination

ABSTRACT

A method using various heuristics techniques for resolving ambiguity in location determination in environments with or without noise. A final location determination solution may be determined from a set of ambiguous location determination solutions by using clock temporal bias value, by using consistency information of ranging signal order such as the time of arrival and/or the received power level of the ranging signals, by using the distances to the sources, and/or by using other discriminator functions to select the final location determination solution from a plurality of ambiguous location determination solutions. The main advantage of the heuristic approaches is that redundant measurements are not required for location determination solution disambiguation.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a divisional application of U.S. patent applicationSer. No. 11/034,705, filed on Jan. 12, 2005, which claims benefit ofpriority from U.S. Provisional Patent Application No. 60/629,011, filedNov. 17, 2004, each of which is incorporated by reference for allpurposes.

BACKGROUND OF THE INVENTION

1. Field

The disclosed subject matter relates generally to methods for locationdetermination, and more particularly to methods using various heuristicstechniques for resolving ambiguity in location determination inenvironments with or without noise.

2. Background

In range-based location determination systems, measurements of rangingsignals from a plurality of sources are converted to distanceinformation associated with the source of each ranging signal. Distancesto different sources with known locations are combined to solve for theunknown user location via geometric techniques known, for example astrilateration (a.k.a. triangulation). If delay of ranging signals cannotbe known reliably (e.g. in asynchronous systems where the user clock isnot synchronized to the network), location determination algorithms maytreat user clock temporal bias as another unknown, to be solved for bythe trilateration process, using an additional measurement.

However, a location determination system is ambiguous if more than onelocation determination solution set of user coordinates and clocktemporal bias is consistent with a set of distance measurements.Location determination systems can produce ambiguous locationdetermination solutions in three distinct ways: first, ambiguity can becaused by insufficient measurements; second, ambiguity may be introducedby the properties of the algorithm employed in location determination;and third, ambiguity may be introduced by the presence of noisymeasurements.

First, a system has an insufficient number of measurements when thenumber of unknowns is greater than or equal to the number of independentmeasurements. For example, consider the case where the unknowns are thetwo-dimensional user spatial coordinates and user clock temporal bias.Consider the case depicted in FIG. 1. There are three unknowns, namelythe mobile station latitude, longitude and clock temporal bias. Thereare three base stations, namely BS1, BS2 and BS3, and three associateddistance measurements. Circles are plotted centered at a particular basestation, with radii given by the sum of the distance between the mobilestation and the base station as measured at the mobile station, and thecomputed clock temporal bias corresponding to a fitting solution. Giventhree independent distance measurements, there are two possible locationdetermination solutions, depicted at the intersection of each set ofcircles.

Second, the nature of the algorithm used for locating a user can also bea source of ambiguity. A well known algorithm that is susceptible toambiguity is described in the U.S. Pat. No. 6,289,280. This algorithmsolves for unknowns using a closed form system of equations. Because itsolves for the user location algebraically, this algorithm runsefficiently, making it suitable to applications and devices with time orresource constraints. The solution uses linear algebra manipulations tocombine the measurements into a system of quadratic equations where thenumber of equations equals the number of unknowns. Two solutions areproduced associated with the two roots of the quadratic equations. Thetwo solutions form an ambiguous set of solutions which needs to beresolved by additional means.

For example, consider the case where the unknowns are thetwo-dimensional user spatial coordinates and user clock temporal bias.With four measurements, the system can be said to have a sufficientnumber of measurements to unambiguously solve for the user location.Yet, when the algebraic method is used, the four measurements arecombined into three “average” measurements and two solutionscorresponding to these averages are identified, as shown in FIG. 2.

Third, noisy measurements can lead to error in the determination of userlocation. Consider a method for location determination in noisyenvironments by assuming the noise to be a discrete variable with knownor computable statistical parameters. A set of adjusted measurements andcorresponding solution are generated for each assumed noise level. Suchlocation determination system is ambiguous, thus also warrantingambiguity resolution techniques. For example, in FIG. 3, consider threenoise levels, each 100 meters apart, associated with the measurementfrom base station BS2. For each noise level, a set of circles is plottedas before, with a radius corresponding to the sum of distancemeasurement (in the case of BS2, this measurement is adjusted by theassumed noise level) and the clock temporal bias computed. There arethree ambiguous solutions, associated with each noise level, shown bythe intersection of circles.

Accordingly, since more than one possible solution is presented by theseprior art algorithms, it would be desirable to provide a method forselecting the correct (a.k.a. final) location determination solutionfrom a set of ambiguous location determination solutions.

BRIEF SUMMARY OF THE INVENTION

Disclosed are methods for selecting the correct (a.k.a. final) locationdetermination solution from a set of ambiguous location determinationsolutions using various heuristics and/or noise removal.

According to one aspect, a method for resolving ambiguity in locationdetermination with N ambiguous location determination solutions usingclock temporal bias value includes the following steps: generating aprobability distribution function (PDF) model for clock temporal bias,obtaining N clock temporal bias values for the N ambiguous locationdetermination solutions, inserting each of the N clock temporal biasvalues into the PDF model, evaluating the PDF model to get N PDF values,setting N goodness metrics to the N PDF values, comparing the N goodnessmetrics, defining a maximum goodness metric as the largest of the Ngoodness metrics, and selecting a final location determination solutionhaving the maximum goodness metric.

According to another aspect, a method for resolving ambiguity inlocation determination with a plurality of ambiguous locationdetermination solutions using the order of receipt (a.k.a. time ofarrival) of a plurality of ranging signals includes the following steps:ranking the plurality of ranging signals based on the order of actualtime of arrival (TOA) from earliest to latest, ranking the plurality ofranging signals based on an expected order of time of arrivalcorresponding to each of the plurality of ambiguous locationdetermination solutions, and comparing the ranking of the rangingsignals based on the order of the actual time of arrival (TOA) and basedon the expected order of time of arrival corresponding to each of theplurality of ambiguous location determination solutions.

According to yet another one aspect, a method for resolving ambiguity inlocation determination with a plurality of ambiguous locationdetermination solutions using the order of received power levels of aplurality of ranging signals includes the following steps: ranking theplurality of ranging signals based on the order of received power levelsfrom strongest to weakest, ranking the plurality of ranging signalsbased on an expected order of received power levels corresponding toeach of the plurality of ambiguous location determination solutions, andcomparing the ranking of the ranging signals based on the order ofreceived power levels and based on the expected order of received powerlevels corresponding to each of the plurality of ambiguous locationdetermination solutions.

According to still another aspect, a method for resolving ambiguity inlocation determination with a plurality of ambiguous locationdetermination solutions includes the following steps: deriving aplurality of distances D1 corresponding to each of the plurality ofambiguous location determination solutions for each of M sources,obtaining a plurality of original distances D2, comparing each of theplurality of distances D1 with each of its corresponding plurality oforiginal distances D2 and computing a plurality of error measurements,and selecting a final location determination solution as having thelowest value of the plurality of error measurements.

According to yet another aspect, a method for resolving ambiguity inlocation determination in a noisy environment includes the followingsteps: selecting L noise levels iteratively within a range [A, B] withan increment I for a first of Q ranging signals, repeating the selectingstep Q minus 1 times for each of the rest of the Q ranging signals toproduce a plurality of L noise levels, creating a plurality of ambiguouslocation determination solutions for each of the plurality of L noiselevels based on a geometric technique, selecting the final locationdetermination solution from the plurality of ambiguous locationdetermination solutions based on a discriminator function.

It is understood that other embodiments will become readily apparent tothose skilled in the art from the following detailed description,wherein it is shown and described various embodiments by way ofillustration. The drawings and detailed description are to be regardedas illustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 graphically illustrates an example of three measurements withunknown user spatial coordinates and clock temporal bias value with twoambiguous location determination solutions.

FIG. 2 graphically illustrates an example of four measurements withthree unknowns with two ambiguous location determination solutions.

FIG. 3 graphically illustrates an example of noisy measurements withthree ambiguous location determination solutions.

FIG. 4 graphically illustrates the relationship between the clocktemporal bias value and the horizontal error for each of the ambiguouslocation determination solutions for the Manhattan dataset.

FIG. 5 graphically illustrates the statistical performance of the twoambiguous location determination solutions (solution #1 and solution #2)and the chosen final location determination solution by resolvingambiguity through clock temporal bias value constraints and is plottedas a cumulative distribution function (CDF) of the horizontal errors inmeters for the Manhattan dataset.

FIG. 6 graphically illustrates the histogram of the clock temporal biasproduced by each of the two ambiguous location determination solutions(solution #1 and solution #2) for the Manhattan dataset.

FIG. 7 graphically illustrates the relationship between the clocktemporal bias value and the horizontal error for each of the ambiguouslocation determination solutions for the Campbell dataset.

FIG. 8 graphically illustrates the statistical performance of the twoambiguous location determination solutions (solution #1 and solution #2)and the chosen final location determination solution by resolvingambiguity through clock temporal bias value constraints and is plottedas a cumulative distribution function (CDF) of the horizontal errors inmeters for the Campbell dataset.

FIG. 9 graphically illustrates the relationship between the clocktemporal bias value and the horizontal error for each of the ambiguouslocation determination solutions for the Japan dataset.

FIG. 10 graphically illustrates the statistical performance of the twoambiguous location determination solutions (solution #1 and solution #2)and the chosen final location determination solution by resolvingambiguity through clock temporal bias value constraints and is plottedas a cumulative distribution function (CDF) of the horizontal errors inmeters for the Japan dataset.

FIG. 11 graphically illustrates the statistical performance of the twoambiguous location determination solutions (solution #1 and solution #2)and the chosen final location determination solution by resolvingambiguity through consistency in order of ranging signal arrival and isplotted as a cumulative distribution function (CDF) of the horizontalerrors in meters for the Manhattan dataset.

FIG. 12 graphically illustrates the statistical performance of the twoambiguous location determination solutions (solution #1 and solution #2)and the chosen final location determination solution by resolvingambiguity through consistency in order of ranging signal arrival and isplotted as a cumulative distribution function (CDF) of the horizontalerrors in meters for the Japan dataset.

FIG. 13 graphically illustrates the statistical performance of the twoambiguous location determination solutions (solution #1 and solution #2)and the chosen final location determination solution by resolvingambiguity through consistency of distance values and is plotted as acumulative distribution function (CDF) of the horizontal errors inmeters for the Manhattan dataset.

FIG. 14 graphically illustrates the statistical performance of the twoambiguous location determination solutions (solution #1 and solution #2)and the chosen final location determination solution by resolvingambiguity through consistency of distance values and is plotted as acumulative distribution function (CDF) of the horizontal errors inmeters for the Japan dataset.

FIG. 15 graphically illustrates the statistical performance of the twoambiguous location determination solutions (solution #1 and solution #2)with the best ambiguous location determination solutions with andwithout noise removal and are plotted as a cumulative distributionfunction (CDF) of the horizontal errors in meters for the Manhattandataset.

DETAILED DESCRIPTION OF THE INVENTION

The description set forth below in connection with the appended drawingsis intended as a description of various embodiments of the invention andis not intended to represent the only embodiments in which the inventionmay be practiced. Each embodiment is provided merely as an example orillustration, and should not necessarily be construed as preferred oradvantageous over other embodiments. Specific details are used toprovide an understanding of the invention. However, it will be apparentto those skilled in the art that the invention may be practiced withoutthese specific details. Acronyms and other descriptive terminology maybe used merely for convenience and clarity and are not intended to limitthe scope of the invention.

Various heuristic approaches for ambiguity resolution are disclosed thatexploit known or expected behavior of one or more unknowns orcombinations thereof, consistency of various quantifiable parameters,and/or redundancy from measurements or different parameters within ameasurement. A main advantage of the heuristic approaches disclosed isthat redundant measurements are not required for the purpose of locationdetermination solution disambiguation.

For the purpose of illustration only, simulations using real user datafrom Manhattan, Japan, and Campbell are presented. The Manhattandatabase consists of 1316 fixes from about 25 locations across the city,with about 20 to 100 position fixes per location. The Japan datasetconsists of about 1912 fixes from all over Japan. The Campbell datasetconsists of a stationary location with about 2000 fixes.

In one embodiment, the method for ambiguity resolution uses physicalconstraints on the behavior of some of the unknowns and combinationsthereof. In particular, asynchronous systems can benefit from a set ofassumptions or information concerning clock temporal bias value, whichcan be exploited to disambiguate the user location. In a system wherethe user clock timing is learned from some reference timing signal, userclock temporal bias value may include the time the reference timingsignal takes to propagate from the source of the reference timing signalto the user, some multipath and receiver processing overhead and clockslewing error. A probability distribution function (PDF) of clocktemporal bias value can be derived for that network and used todisambiguate the user location.

User clock temporal bias values may be characterized by knownstatistical distributions, with distribution parameters dictated byvarious factors, such as the receiver design, the user clock designcharacteristics, multipath characteristics, and, in a terrestrialsystem, by network deployment issues such as base station density. Thedistribution parameters may be set statically or learned dynamically fora given location system. The goodness metric of a final locationdetermination solution can be derived from the value of the computedclock temporal bias value and the probability distribution function(PDF) of clock temporal bias value for that scenario.

To determine the final location determination solution from a set of Nambiguous location determination solutions using N clock temporal biasvalues, the following steps are needed: First, generate a probabilitydistribution function (PDF) model for the N clock temporal bias values.In one embodiment, the PDF model is parameterized by its mean andstandard deviation. Second, for each of the N ambiguous locationdetermination solutions, obtain a clock temporal bias value, resultingin N clock temporal bias value. Third, for each of the ambiguouslocation determination solutions, insert its corresponding clocktemporal bias value into the PDF model. Fourth, for each of theambiguous location determination solutions, evaluate the PDF model atthe inserted clock temporal bias value to get a PDF value. The resultshould be N PDF values for the N clock temporal bias values. Fifth, foreach of the ambiguous location determination solutions, set the goodnessmetric of the ambiguous location determination solution to the PDFvalue. Again, there should be N goodness metrics corresponding to the NPDF values which correspond to the N clock temporal bias values. Sixth,compare the N goodness metrics and defined a maximum goodness metric asthe largest of the N goodness metrics. Seventh, select the finallocation determination solution from the ambiguous locationdetermination solution with the maximum goodness metric. The quantity Nis defined as an integer greater than one.

In one embodiment, clock temporal bias value is modeled to follow aGaussian probability distribution function (PDF) with a mean andstandard deviation of minus 100 meters and 100 meters respectively. Ifthe computed clock temporal bias value (multiplied by the speed oflight) is minus 200 meters, then goodness metric can be set to the valueof the Gaussian probability distribution function one standard deviationaway from the mean. One skilled in the art would know that other PDFsother than a Gaussian PDF (for example, a finite set of statisticalparameters) may be used.

In an example, clock temporal bias value is assumed to have a static,simple uniform distribution, with permissible clock temporal bias valuebetween some value A and another value B. For Manhattan and Campbelldatasets, A and B are set to minus 1000 meter and 1000 metersrespectively. In the case of Japan, A and B are set to minus 2000 metersand 100 meters to better suit the Japan dataset as observed empirically.For the Manhattan dataset, the relationship between clock temporal biasvalue and the horizontal error of each ambiguous location determinationsolution is shown in FIG. 4. For large errors, it is observed that thereis a strong, generally linear dependence of error on clock temporal biasvalue. For the Manhattan dataset, the performance results are shown inFIG. 5. It is observed that the disambiguation algorithm works well inthat the final location determination solution statistically outperformseither of the two ambiguous location determination solutions across theentire dataset. It is noted that the ambiguous location determinationsolution labeled “Solution 2” generally outperforms the ambiguouslocation determination solution labeled as “Solution 1”. Suchdiscrepancy is apparent and explainable by the behavior of clocktemporal bias value as produced by each ambiguous location determinationsolution, and shown in FIG. 6. One can see how the second ambiguouslocation determination solution has a “tight” distribution while thefirst ambiguous location determination solution produced highly-varyingresults.

FIG. 7 graphically illustrates the relationship between the clocktemporal bias value and the horizontal error for each of the ambiguouslocation determination solutions for the Campbell dataset. FIG. 8graphically illustrates the statistical performance of the two ambiguouslocation determination solutions (solution #1 and solution #2) and thechosen final location determination solution plotted as a cumulativedistribution function (CDF) of the horizontal errors in meters for theCampbell dataset. For the Japan dataset, FIG. 9 graphically illustratesthe relationship between the clock temporal bias value and thehorizontal error for each of the ambiguous location determinationsolutions while FIG. 10 graphically illustrates the statisticalperformance of the two ambiguous location determination solutions(solution #1 and solution #2) and the chosen final locationdetermination solution plotted as a cumulative distribution function(CDF) of the horizontal errors in meters.

Alternative methods for ambiguity resolution use consistency of rangingsignal order. There are various ways for deriving ranging signal order,such as the time of arrival (TOA) of ranging signals and/or the powerlevel of ranging signals.

In one embodiment, the method for ambiguity resolution uses the order ofreceipt (a.k.a. time of arrival) of ranging signals from a plurality ofsources to resolve ambiguity. The expected order corresponding to eachsolution is derived and compared to the actual order in which theranging signals are received. The chosen final location determinationsolution is the one with the closest match in ordering, or a combinationof a number of ambiguous location determination solutions with theclosest match in ordering.

To determine the final location determination solution from a set ofambiguous location determination solutions using the order of receipt(a.k.a. time of arrival) of ranging signals, the following steps areneeded: First, rank the ranging signals based on the order of actualtime of arrival (TOA) from earliest to latest. Second, for each of theambiguous location determination solution, rank the ranging signalsbased on the expected order of time of arrival corresponding to eachambiguous location determination solution. Third, compare the ranking ofthe ranging signals in the order of the actual time of arrival (TOA)from earliest to latest to the ranking of the ranging signals based onthe expected order of arrival corresponding to each ambiguous locationdetermination solution. Fourth, select the final location determinationsolution as the one with the closest match in ordering, or a combinationof a number of ambiguous location determination solutions with theclosest match in ordering.

In an example, the a-posteriori ranging signal order is compared to thea-priori ranging signal order derived from the time-of-arrival (TOA)information. For each ranging signal, its rankings in the a-priori anda-posteriori lists are compared and labeled a “1” in case of a match anda “0” in case of a mismatch. The solution with the most matches (i.e.,the most “1”s) across all ranging signals is picked as the locationdetermination solution. In case of a tie, the average is used. Theresults for this disambiguation technique applied to the Manhattandataset are shown in FIG. 11 and for the Japan dataset are shown in FIG.12. In general, the final location determination solution faressimilarly or statistically outperforms either of the two ambiguouslocation determination solutions.

In another embodiment, the method for ambiguity resolution uses thereceived power level of ranging signals to discriminate among ambiguouslocation determination solutions. One can use received power level todiscriminate among location determination solutions. To determine thefinal location determination solution from a set of ambiguous locationdetermination solutions using the received power levels of the rangingsignals, the following steps are needed: First, rank the ranging signalsbased on the order of the received power level received, from strongestto weakest. Second, for each of the ambiguous location determinationsolution, rank the ranging signals based on the expected order of thereceived power levels corresponding to each ambiguous locationdetermination solution. The expected order of the received power levelsis assumed to be the same as the expected order of the time of arrivalcorresponding to each ambiguous location determination solution. Third,compare the ranking of the ranging signals in the order of the receivedpower level received from strongest to weakest to the ranking of theranging signals based on the expected order of the received power levelscorresponding to each ambiguous location determination solution. Fourth,select the final location determination solution as the one with theclosest match in ordering, or a combination of a number of ambiguouslocation determination solutions with the closest match in ordering.

In another embodiment, the method for ambiguity resolution uses thenumerical values of the distances to M sources and combinations thereofto resolve ambiguity. One can derive each of the distance D1 to each ofthe M sources corresponding to each ambiguous location determinationsolution and compare them to the original distance adjusted by the clocktemporal bias value (labeled as original distance D2). The quantity ofsources is defined as M whereby M is an integer greater than one. Theambiguous location determination solution with the closest match (asdefined by the root-mean-square of D1 minus D2 or by the mean square ofD1 minus D2), or some combination of a number of values with the closestmatches is chosen as the final location determination solution. It willbe understood by one skilled in the art that the present invention isnot confined to the root-mean-square or mean-square of D1 minus D2 andthat other error measures such as, but not limited to, root-sum-square(RSS) or sum-square (SS) may also be used without violating the spiritof the invention. The results for the Manhattan and Japan dataset areshown in FIG. 13 and FIG. 14 respectively. In both cases, the finallocation determination solution statistically outperforms either of thetwo ambiguous location determination solutions across the entiredataset.

To determine the final location determination solution from a set ofambiguous location determination solutions using numerical values of thedistances to the M sources, the following steps are needed: First, foreach of the M sources, derive the distance D1 corresponding to eachambiguous location determination solution. Second, obtain the originaldistance D2 (which is the original distance adjusted by clock temporalbias value). Third, compare the distance D1 with the original distanceD2 and compute their error measurement. In one embodiment, the errormeasurement is the root-mean-square of D1-D2. In another embodiment, theerror measurement is the root-sum-square of D1-D2. And, in yet anotherembodiment, the error measurement is the root-mean-square of D1-D2normalized by the size of the vector D1 (or D2). Fourth, select thefinal location determination solution as having the lowest value oferror measurement.

Noisy measurements can also lead to errors in location determination.Out of a set of P signals, a subset of Q signals is selected by somemethod so that the algorithm may run efficiently. The selection methodmay select the Q signals randomly or via a more systematic method, suchas choosing those signals with the lowest power level (that are morelikely to incur noise overhead). For each of the Q signals selected, Lnoise levels are postulated. The location determination solutionscorrespond to each of L times Q combinations of noise levels. A methodfor noise disambiguation involves the following steps. First, for thefirst of the Q ranging signals, select L noise levels iteratively withina range [A, B] with an increment I. One skilled in the art would knowthe range [A, B] and the increment I (and hence, the quantity of L)based on the chosen location determination system. Second, repeat theabove step Q minus 1 times for each of the rest of the Q minus 1 rangingsignals to produce a plurality of L noise levels. Third, create aplurality of ambiguous location determination solutions for each of theplurality of noise levels based on known geometric techniques such astrilateration. Fourth, a discriminator function, known to one skilled inthe art, is used to select the final location determination solutionfrom the plurality of ambiguous location determination solutionsproduced for each of the plurality of noise levels. The discriminatorfunction can use one or a combination of goodness metrics such as powerranking, distance values, clock temporal bias constraints, order ofreceipt of the ranging signals. In one embodiment, the discriminatorfunction jointly maximizes the consistency of power ranking and distancevalues, while meeting the clock temporal bias constraint. Otherdiscriminator functions are known to one skilled in the art and may beused without departing from the spirit of the present invention.

In an example, the noise removal techniques for fixes in the Manhattandataset. N, A, B are set to 2, 0, and 500 meters respectively. Theincrement I values is investigated in the set of {100 meters, 50meters}. Disambiguation among solutions is done by means of acombination of power ranking, range consistency and clock temporal biasvalue. The power ranking algorithm works as follows: Using the powerlevel of incoming ranging signals, the a-priori order of ranging signalsis computed. Then, for each solution corresponding to a noise setting,the a-posteriori order of ranging signals is computed using geographicaldistances. The a-priori and a-posteriori orders are compared for a givenranging signal. Its ranking in the a-priori and a-posteriori lists arecompared and a match is labeled a “1” while a mismatch returns a “0.”The power ranking is computed as the sum of matches across all rangingsignals, weighted for each ranging signal in the following way: if theranging signal is the first ranging signal in the a-priori list (thatis, for the base station which is assumed closest), then the weight is½. Otherwise, the weight is 0.5 times (the number of ranging signals−1). Note the weights are normalized, as they add up to 1. Also, thebias in the weighting allows the strongest ranging signal to be moreheavily weighted which has been shown to give improved results. Theroot-mean-square differences in the a-priori and a-posteriori ranges(RRMS) are also computed for each solution. Range consistency iscalculated as 1—RRMS/max(RRMS). The clock temporal bias value enforces a[−1000 meters to 1000 meters] range on the accepted solutions. The finallocation determination solution is the one that meets the clock temporalbias value constraint while maximizing the product of the power rankingand range consistency. The results are shown in FIG. 15. It is notedthat an improvement is observed over the best algebraic method.

The previous description of the disclosed embodiments is provided toenable any person skilled in the art to make or use the presentinvention. Various modifications to these embodiments will be readilyapparent to those skilled in the art, and the generic principles definedherein may be applied to other embodiments without departing from thespirit or scope of the invention.

1. A method for resolving ambiguity in location determination with aplurality of ambiguous location determination solutions for a mobilestation using an order of received power levels of a plurality ofranging signals from base stations in a cellular network, the methodcomprising: receiving the plurality of ranging signals at the mobilestation; ranking the plurality of ranging signals based on the order ofreceived power levels from strongest to weakest; ranking the pluralityof ranging signals based on an expected order of received power levelscorresponding to each of the plurality of ambiguous locationdetermination solutions; comparing the ranking of the ranging signalsbased on the order of received power levels and based on the expectedorder of received power levels corresponding to each of the plurality ofambiguous location determination solutions; and determining a finallocation determination solution based on a result of comparing therankings of the ranging signals.
 2. The method of claim 1, whereindetermining the final location determination solution comprisesdetermining the ambiguous location determination solution, from theplurality of ambiguous location determination solutions, for which theexpected order of received power levels is a closest match to the orderof received power levels.
 3. The method of claim 1, wherein determiningthe final location determination solution comprises determining acombination of the plurality of ambiguous location determinationsolutions with a closest match in ordering.
 4. A method of determining afinal location determination solution for a mobile station from aplurality of candidate location determination solutions, the methodcomprising: receiving ranging signals from a plurality of sources at themobile station; determining a received power level for each of theranging signals; ranking the ranging signals based on the received powerlevel for each of the ranging signals; determining an expected order ofreceived power levels for each of the candidate location determinationsolutions; determining a preferred candidate location determinationsolution from the plurality of candidate location determinationsolutions for which the expected order of received power levels is aclosest match to the ranking of the ranging signals based on thereceived power level; and determining the final location determinationsolution for the mobile station based on the preferred candidatelocation determination solution.
 5. The method of claim 4, furthercomprising: summing a number of matches between the ranging signalsranked based on the received power levels and the ranging signals rankedbased on the expected order of the received power levels for each of thecandidate location determination solutions.
 6. The method of claim 5,wherein summing the number of matches comprises weighting each rangingsignal according to a corresponding position in the expected order ofthe received power levels.
 7. The method of claim 5, wherein thepreferred candidate location determination solution has a greatestnumber of the matches among the plurality of candidate locationdetermination solutions.
 8. The method of claim 4, wherein each of theplurality of candidate location determination solutions corresponds to adistinct noise level.
 9. A method for resolving ambiguity in locationdetermination with a plurality of ambiguous location determinationsolutions for a mobile station using an order of received power levelsof a plurality of ranging signals transmitted by base stations in acellular network, the method comprising: receiving the ranging signalsat the mobile station from the base stations; determining a receivedpower level for each of the ranging signals; ranking the ranging signalsbased on the order of received power levels from strongest to weakest;determining an expected order of the received power levels for each ofthe ambiguous location determination solutions; assigning a score toeach of the ambiguous location determination solutions based on a numberof matches between the ranging signals in the expected order of thereceived power levels corresponding to each of the ambiguous locationdetermination solutions and the ranging signals ranked based on theorder of the received power levels; and determining a final locationdetermination solution for the mobile stations based on the scoresassigned to the ambiguous location determination solutions.
 10. Themethod of claim 9, wherein assigning the score to each of the ambiguouslocation determination solutions comprises weighting each of the rangingsignals according to a corresponding position in a corresponding one ofthe expected orders of the received power levels.
 11. The method ofclaim 9, wherein each of the plurality of ambiguous locationdetermination solutions corresponds to a distinct noise level.
 12. Acomputer-readable medium having stored thereon a processor-readableprogram of instructions for causing a processor to resolve ambiguity inlocation determination with a plurality of ambiguous locationdetermination solutions for a mobile station using an order of receivedpower levels of a plurality of ranging signals, the program ofinstructions being configured to cause the processor to: form a firstranking of the plurality of ranging signals based on the order ofreceived power levels from strongest to weakest; form a second rankingof the plurality of ranging signals based on an expected order ofreceived power levels corresponding to each of the plurality ofambiguous location determination solutions; compare the first rankingwith each of the second rankings; and determine a final locationdetermination solution for the mobile station from the plurality ofambiguous location determination solutions based on a result ofcomparing the first ranking with the second rankings.
 13. The computerreadable medium of claim 13, wherein the program further comprisesinstructions configured to cause the processor to select as the finallocation determination solution an ambiguous location determinationsolution for which the expected order of the received power levels is aclosest match to the order of the received power levels.
 14. Thecomputer readable medium of claim 13, wherein the program furthercomprises instructions configured to cause the processor to select asthe final location determination solution a combination of the pluralityof ambiguous location determination solutions with a closest match inordering.